The present disclosure relates to systems and methods for image-based (also known as “vision-aided”) navigation and more particularly relates to image-based navigation.
A global navigation satellite system (GNSS) can be used to provide navigation information, e.g., position and velocity measurements, for a mobile platform such as a vehicle. When the mobile platform is in a GNSS-denied environment, an inertial navigation system can be used to provide position, attitude and velocity measurements or estimates. However, in a typical inertial navigation system, the errors in estimated position increase with time.
In view of the challenges presented by GNSS-based and inertial navigation, some vehicles use methods of navigation that estimate spatial position based on visual indicators. Image-based navigation uses the angular measurements of reference points on the Earth's surface to determine vehicle attitude, vehicle position and time. Spatial position may be estimated, for example, by comparing captured images to images stored in a database.
A typical image-based navigation system includes a monocular camera and an image processor that is configured to match an acquired image to a reference image. Such image matching can be used, for example, to determine the position of a camera-equipped vehicle relative to the Earth-centered Earth-fixed (ECEF) coordinate system. The locations of matched images can be fed to a Kalman filter along with the position, velocity, and attitude calculated by the inertial navigation system. The Kalman filter fuses information from the inertial and image-based navigation systems to generate optimal estimates of the state of the vehicle.
One challenge posed by image-based navigation occurs when a vehicle travels over information-poor topography (e.g., water or desert). When part of the captured image contains insufficient information for matching, the quality of the overall measurement may be degraded.